Optimization of Turning Parameters of Cryogenic Soaked AZ91 Magnesium Alloy using TOPSIS coupled Taguchi Technique
Bibliographic record
Abstract
Magnesium alloys is a lightweight material establishing scope in various fields such as aerospace and automobile. Moreover, magnesium alloys are also considered as a hydrogen storage materials. In order to understand the machining behaviour of magnesium alloy a detail research is planned. In this research the turning experiments are conducted on AZ91 magnesium alloy using the L9 orthogonal array (OA) experimental design. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) coupled with Taguchi and Analysis of Variance (ANOVA) methods are used to analysis the experiments. The influence of cryogenic soaking duration, feed, speed and depth of cut on cutting temperature, surface roughness and cutting force were studied. Based on the TOPSIS analysis the optimal combination for the better response is cryogenic soaking time of 30 min, speed of 500 rpm, feed of 40 mm/min and depth of cut of 0.3mm. As per F value the depth of cut shows the 0.4822 and next best factors are cutting speed and feed rate. The chip analysis reveals that the cryogenic soaking duration level of 60 min is found to reduce the self-ignited chips.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".